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Volume 22 Issue 2
Mar.  2000
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Tian Yuan, Yang Haijun, Liang Dequn, Wang Hongguan, Wu Gengshi. THE METHOD OF EDGE DETECTION BASED ON NEURAL NETWORK AND THE MEASURE OF IMPORTANCE OF POINTS[J]. Journal of Electronics & Information Technology, 2000, 22(2): 247-252.
Citation: Tian Yuan, Yang Haijun, Liang Dequn, Wang Hongguan, Wu Gengshi. THE METHOD OF EDGE DETECTION BASED ON NEURAL NETWORK AND THE MEASURE OF IMPORTANCE OF POINTS[J]. Journal of Electronics & Information Technology, 2000, 22(2): 247-252.

THE METHOD OF EDGE DETECTION BASED ON NEURAL NETWORK AND THE MEASURE OF IMPORTANCE OF POINTS

  • Received Date: 1998-06-22
  • Rev Recd Date: 1999-02-12
  • Publish Date: 2000-03-19
  • Based on the scale and the state of intensity distribution in some neighborhoods of edge points, this paper proposes a measure vector of importance of point that consists of 4 components and corresponds to every point. For considering the background of application, this paper first trains a BP neural network using some samples that have classified by manual work, and then extracts more important edge points in a new image using the trained neural network. Because the image needs not be smoothed by some function in this algorithm, the edge deflection will not happen as usual, the location of gotten edge is in the accurate position. The effectiveness of this algorithm has been testified by some experiments.
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  • Mallat S. et al. Characterisitics of signals from multiscale edges. IEEE Trans. on PAMI, 1992,PAMI-14(7): 710-732.[2]Witkin A. Scale-space filtering, in Proc. Int. Joint Conf. Artifical Intell., Karlsrhue, Germany:1983, 1019-1022.[3]Bergholm F, Edge focusing, IEEE Trans. on PAMI, 1987, PAMI-9(6), 726-741.[4]杨烜,梁德群.基于方向信息的多尺度边缘检测方法,西安电子科技大学学报,1997,24(4),524-530[5]焦李成.神经网络理论.西安:西安电子科技大学出版社,1990,112-200.
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